用扩散模型扩充小样本风格数据，实现快速高质量人脸风格迁移。
Styleclone: Face Stylization with Diffusion Based Data Augmentation
- 结合文本反演与扩散生成，自动扩增有限风格图像
- 在多风格测试中提升内容保真度与推理速度
- 适合资源受限但需高质量风格迁移的场景
我们提出StyleClone，一种在少量风格图像下训练图像到图像翻译网络以实现特定风格人脸转换的方法。该方法利用文本反演和基于扩散的引导图像生成技术，对小规模风格数据集进行增强。通过系统性地生成受原始风格图和真实人脸图双重引导的多样化风格样本，显著提升了风格数据集的多样性。基于此增强数据集，训练出的快速图像到图像翻译网络在速度与质量上均优于扩散类方法。多个风格的实验表明，本方法能有效提升风格化质量，更好保留源图像内容，并大幅加速推理过程。此外，我们还系统评估了不同增强技术对风格化性能的影响。
原文摘要 · Abstract (English)
We present StyleClone, a method for training image-to-image translation networks to stylize faces in a specific style, even with limited style images. Our approach leverages textual inversion and diffusion-based guided image generation to augment small style datasets. By systematically generating diverse style samples guided by both the original style images and real face images, we significantly enhance the diversity of the style dataset. Using this augmented dataset, we train fast image-to-image translation networks that outperform diffusion-based methods in speed and quality. Experiments on multiple styles demonstrate that our method improves stylization quality, better preserves source image content, and significantly accelerates inference. Additionally, we provide a systematic evaluation of the augmentation techniques and their impact on stylization performance.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。